Báo cáo toán học: " Protection of primary users in dynamically varying radio environment: practical solutions and challenges" pot

This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted
PDF and full text (HTML) versions will be made available soon.
Protection of primary users in dynamically varying radio environment: practical
solutions and challenges
EURASIP Journal on Wireless Communications and Networking 2012,
2012:23 doi:10.1186/1687-1499-2012-23
Pawel Kryszkiewicz ()
Hanna Bogucka ()
Alexander M Wyglinski ()
ISSN 1687-1499
Article type Research
Submission date 20 May 2011
Acceptance date 20 January 2012
Publication date 20 January 2012
Article URL />This peer-reviewed article was published immediately upon acceptance. It can be downloaded,
printed and distributed freely for any purposes (see copyright notice below).
For information about publishing your research in EURASIP WCN go to
/>For information about other SpringerOpen publications go to

EURASIP Journal on Wireless
Communications and
Networking
© 2012 Kryszkiewicz et al. ; licensee Springer.
This is an open access article distributed under the terms of the Creative Commons Attribution License ( />which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Protection of primary users in dynamically varying
radio environment: practical solutions and chal-
lenges
Pawel Kryszkiewicz
1
, Hanna Bogucka
∗1

and Alexander M Wyglinski
2
1
Chair of Wireless Communications, Poznan University of Technology, Poznan, Poland
2
Wireless Innovation Laboratory, Worcester Polytechnic Institute, Worcester, MA, USA
∗
Corresponding author:
Email addresses:
PK:
AMW:
Abstract
1
One of the primary objectives of deploying cognitive radio (CR) within a dynamic spec-
trum access (DSA) network is to ensure that the legacy rights of incumbent licensed (primary)
transmissions are protected with respect to interference mitigation when unlicensed (secondary)
communications are simultaneously operating within the same spectral vicinity. In this article, we
present non-contiguous orthogonal frequency division multiplexing (NC-OFDM) as a promising
and practical approach for achieving spectrally agile wireless data transmission that is suitable for
secondary users (SUs) to access fragmented spectral opportunities more efficiently. Furthermore,
a review of the current state-of-the-art is conducted with respect to methods specifically de-
signed to protect the transmissions of the primary users (PUs) from possible interference caused
by nearby SU transceivers employing NC-OFDM. These methods focus on the suppression of
out-of-band (OOB) emissions resulting from the use of NC-OFDM transmission. To achieve
the required OOB suppression, we present two practical approaches that can be employed in NC-
OFDM, namely, the insertion of cancellation carriers and windowing. In addition to the theoretical
development and proposed improvements of these approaches the computer simulation results of
their performance are presented. Several real-world scenarios regarding the coexistence of both
PU and SU signals are also studied using actual wireless experiments based on software-defined
radio. These simulation and experimental results indicate that OOB suppression can be achieved

under real-world conditions, making NC-OFDM transmission a viable option for CR usage in DSA
networks.
1 Introduction
The idea of cognitive radio (CR) encompasses opportunistic and dynamic access to spec-
trum resources that might be available at a certain location and time. These resources,
called spectrum holes, especially in metropolitan areas, can potentially be fragmented with
several non-contiguous spectral bands of different width. Moreover, the availability of these
spectrum holes may dynamically change over time, as the licensed users (primary users—
PUs) enter into and depart from a given location. There has been a substantial amount
of research conducted with respect to finding suitable technologies capable of aggregating
2
the available spectrum adaptively according to dynamics of spectrum holes availability, and
to support the transmissions of the secondary users (SUs) in a spectrally efficient manner.
In order to use the fragmented spectrum, an SU radio transceiver must be able to shape
its emission to make best use of available resources while simultaneously respecting the
incumbent spectral accessing rights of the PUs.
The key for achieving a spectrally agile waveform that enables the coexistence of both
PU and SU transmissions within a specified spatial, temporal, and spectral vicinity is to
exert strict control over the spectral extent of the transmitted signal. One spectrally agile
waveform approach that has been receiving significant attention in recent years is non-
contiguous orthogonal frequency division multiplexing (NC-OFDM) [1, 2], which is based on
the popular orthogonal frequency division multiplexing (OFDM) transmission technique. One
of the primary advantages of using NC-OFDM within the context of a dynamic spectrum
access (DSA) network is that it provides the flexibility of deactivating, or nulling, specific
subcarriers with zeros as input values such that there is no SU transmit power at frequency
locations corresponding to the presence of PU emissions.
Despite its advantages, NC-OFDM possesses several substantial technical issues that
need to be resolved in order to make this form of wireless data transmission within a DSA
environment a viable option. One of these issues is the shape of the NC-OFDM spectrum
outside of the intended transmission bandwidth, which is known to be relatively high when

left untreated due to the Sinc pulse shapes of the individual data-bearing subcarriers. Con-
sequently, if PU transmissions are located next to a collection of data-bearing subcarriers
belonging to an NC-OFDM signal, this may result in the former experiencing an unaccept-
able level of interference from the latter. Therefore, it is essential that the spectral shape of
the NC-OFDM waveform is treated such that the out-of-band (OOB) radiation is minimized.
In addition to the issue of OOB interference, OFDM-based waveforms are generally char-
acterized by relatively high peak-to-average-power ratio (PAPR), which makes the transmit
signal vulnerable to nonlinear distortions, such as signal clipping in high-power amplifiers.
If signal clipping does occur, the resulting transmission spectrum will broaden, thus yielding
a potential interference situation with adjacent PU signals. Consequently, it is important
to investigate suitable methods for reducing the PAPR of NC-OFDM transceivers with the
3
goal of mitigating OOB interference. There have been a number of articles dealing with
this problem, suggesting either PAPR reduction methods (see an overview of these methods
in [3] and the references therein), signal predistortion (e.g., [4]) or the linearization methods
of a power amplifier. However, further development of these methods is required to make
them sufficiently practical for the purposes of realizing transceiver implementations in actual
real-world scenarios, such that the choice of an appropriate method should be able to handle
the time-varying radio transmission environment, including dynamically changing types of
the PU transmissions. Simultaneously, these methods should aim at achieving reasonable
computational complexity, negligible performance degradation of the SU transmission, and
low energy costs.
In this article, we present an investigation of spectrally agile waveforms based on NC-
OFDM and assess their suitability for achieving SU transmissions that are capable of respect-
ing the rights of incumbent PU signals. In Section 2, we present an overview of NC-OFDM
transmission within the context of a cognitive radio-based DSA network. We then review
in Section 3 existing methods for achieving flexible spectral waveforms using NC-OFDM
while simultaneously mitigating the effects of OOB interference. Section 4 provides a closer
look at a promising technique for mitigating OOB interference that combines the insertion
of so-called cancellation carriers (CCs) with OFDM symbol-based windowing.Moreover,

we present an enhanced optimization algorithm with reduced computational complexity and
reduced energy costs. Finally, the proposed OOB interference reduction approach for NC-
OFDM is evaluated using actual wireless transceivers based on software-defined radio (SDR)
technology within a controlled environment, and the results of these experiments are pre-
sented and discussed in Section 5.
2 Spectrally agile multicarrier waveform framework
A conventional wireless transmission system is usually allocated a specific frequency band
for data communications. These wireless transmissions are usually licensed, which means
they possess exclusive rights to the assigned frequency bands. Although much of the wireless
spectrum up to 3 GHz has been assigned to licensed wireless applications, several measure-
4
ments campaigns have shown that a substantial portion of the licensed frequency bands are
underutilized across the temporal, spectral, and spatial domains [5]. To continue providing
sufficient spectral bandwidth for satisfying both current and future wireless access needs,
both spectrum policy makers and communication technologists have proposed an innova-
tive approach with respect to the wireless spectrum usage via opportunistic spectrum access
(OSA). Relative to traditional approaches for accessing spectrum, OSA allows for unlicensed
wireless users to temporarily “borrow” unoccupied licensed frequency bands [6]. However,
these unlicensed (i.e., secondary) devices must still guarantee interference-free wireless access
to incumbent licensed (i.e., primary) signals. In particular, it is essential that the OOB radi-
ation generated by the SU wireless device is mitigated in order to prevent interference with
PU wireless signals located in the frequency vicinity. Consequently, given this constraint on
SU wireless transceivers, communication systems performing OSA require a level of spectral
agility in order to operate in the presence of PU signals, especially when it comes to mitigat-
ing interference resulting from OOB radiation, as well as simultaneously transmitting across
several unoccupied frequency bands that are fragmented across the wireless spectrum whose
aggregate bandwidth satisfies the secondary transmission requirements.
Multicarrier modulation (MCM) possesses sufficient spectral agility in order to facilitate
the transmission of data from unlicensed SU transmitters across several fragmented fre-
quency bands simultaneously even in the presence of licensed PU signals, thus resulting in

an increase in spectrum utilization [7]. In particular, subcarriers located in the frequency
vicinity of unoccupied wireless spectrum can be used for transmitting data while those sub-
carriers that could potentially interfere with nearby PU signals can be deactivated or nulled.
However, simply deactivating subcarriers for the purposes of OOB interference mitigation
may not be sufficient for the neighboring PUs’ interference tolerance levels. Moreover, in
addition to achieving a required level of OOB interference within a given spectrum mask,
an SU transmitter performing OSA must be capable of tailoring its spectral characteristics
dynamically in order to avoid interference with the dynamically changing incumbent licensed
PU transmissions. Finally, most MCM transmission approaches possess the possibility of
exhibiting large envelope variations in the time domain that is often characterized by a high
PAPR. This results from the combination of the subcarrier signals into a single composite
5
multicarrier waveform in the time domain. When high PAPR occurs, the resulting trans-
mission spectrum broadens and produces OOB interference regardless of whether the initial
spectral waveform has been properly shaped at the transmitter for low OOB interference.
Overall, non-contiguous MCM techniques have been recognized as a suitable candidate
for OSA due to their potential for achieving spectrally efficient communications by exploit-
ing fragmented unoccupied spectrum while simultaneously achieving high data rates [8, 9].
In fact, this form of data transmission approach is well-suited for future wireless commu-
nication systems, including CR systems [10]. As mentioned before, the NC-OFDM scheme
possesses the ability to efficiently use fragmented spectrum opportunities as well as perform
spectrum shaping in order to suppress interference that may affect nearby primary wireless
transmissions. To counteract the potential for significant OOB interference resulting from
NC-OFDM transmission, which can negatively affect neighboring wireless signals, several
techniques have been proposed in the literature that are designed to significantly suppress
these sidelobes in order to make coexistence between PUs and SUs feasible. On the other
hand, the OOB reduction process can potentially increase the computational complexity
and energy (power) utilization. Given the possible constraints of limited computational and
energy resources available via a user equipment, a practical approach to this problem is
needed that achieves a balance between the OOB interference mitigation efficiency and its

associated costs.
3 OOB reduction techniques for spectrally agile multicarrier waveforms
The dilemma of how to mitigate the OOB interference in multicarrier systems has attracted
substantial interest over the last decade. In this section, we present an overview of the major
achievements in this field, and indicate two methods that are particularly attractive for the
application in CR framework.
3.1 State-of-art techniques for OOB radiation reduction
The simplest method for achieving OOB interference reduction is to reserve a number of edge
(guard) subcarriers (GS) to serve as a spectral buffer between PU and SU transmissions [7],
6
i.e., deactivation of subcarriers. Although simple to implement, this method significantly
decreases the spectral efficiency and does not provide sufficient OOB interference reduction
in most scenarios.
Another approach to the OOB power reduction of an OFDM signal is to spectrally shape
each individual subcarrier spectrum [7]. We will discuss this simple method called window-
ing (W) in the following subsection in greater detail. In the adaptive symbol transition
(AST) method [11], similar to W, the time-domain samples in the transition region be-
tween consecutive symbols are chosen adaptively in order to minimize the OOB power. For
the AST algorithm, the information about symbols mapped to each subcarrier is needed
in order to assess the amount of OOB interference in the neighboring frequency bands. A
mean-square-error (MSE) minimization method is used to determine the values of the time-
domain samples in the transition region. The primary drawbacks of this method are high
computational complexity and reduced throughput.
Another method, called constellation expansion (CE) [12], adjusts the modulated data
symbols transmitted per subcarrier such that the OOB interference can be reduced while
simultaneously not losing any data information or causing distortion. This is achieved by
enlarging the modulation constellation and by allowing data symbols to be represented by
any one of the two constellation points. As a result, the minimum distance between the
constellation points is reduced, and the bit-error-rate (BER) performance decreases.
Another method, called subcarriers weighting (SW) [13, 14], minimizes the signal OOB

interference level by multiplying the data subcarriers by optimized real weighting coefficients.
At the receiver, data symbols transmitted using the weighted subcarriers can be viewed as
distorted, particularly for the high values of the weighting coefficients. Consequently, the
authors suggest to impose a constraint on the weighting coefficients values. Simulation
results exhibit significant OOB interference suppression. Some modifications to this method
have been made in [15], where maximization of the channel capacity combined with OOB
interference mitigation is addressed.
In the multiple-choice sequences (MCS) [16] method, for each sequence of data symbols
to be transmitted in an OFDM symbol, a set of corresponding sequences representing it is
calculated. The sequence yielding the lowest interference to adjacent bands is then chosen
7
from this set and transmitted. To retrieve the initial data sequence at the receiver the identi-
fication number of the selected sequence has to be provided, what requires additional control
channel for this side-information. A variant of the MCS method with reduced computational
complexity is presented in [17]. In this method, the corresponding sequences are generated
through the data symbols phases rotation of the multiple of π/2, and thus a limited number
of possible sets of sequences must be examined to choose the optimum one. As the OFDM
edge subcarriers possess the strongest influence on the OOB radiation, only those subcarriers
are altered. Another variant of the MCS method involves its merging with other spectrum
shaping algorithms, e.g., in [18] the authors combined the MCS method with both SW and
CCs method.
Polynomial cancellation coding (PCC) has been proposed in [19] and revisited in [20].
This method not only reduces the OOB radiation but also lowers the OFDM signal sensitivity
to phase and frequency errors. As neighboring subcarriers have firmly aligned spectra, the
adjacent subcarriers are modulated with the same, appropriately scaled data symbol in order
to reduce the sidelobes power. This is usually done for groups of two or three subcarriers.
Although this method reduces the system throughput, this effect can be weakened as the
cyclic prefix (CP) does not have to be added and coded redundancy can be used to increase
SNR.
Another method for achieving OOB interference reduction, called spectral precoding

(SP), has been described in [21, 22]. In this method, the correlation between the data-
symbols transmitted on subcarriers is introduced by block-coding. The code-generating
matrix is chosen so as to minimize the OOB radiation power. The SP method provides the
lowest OOB interference levels relative to other methods simulated in [21]. On the other
hand, it has been observed that the OOB interference suppression is not so high when the
CP is applied.
Another method for reducing OOB interference, called extended active interference can-
cellation (EAIC) [23], is based on the insertion of special carriers that are designed to
negatively combine with high-power sidelobes caused by the data subcarriers. The AIC
subcarriers can be placed inside the adjacent transmission spectrum, usually at frequency
locations that are non-orthogonal to the SU data subcarriers. The main drawback of this
8
method results from this lack of orthogonality and thus, data symbols distortion. A vari-
ant of the EAIC method was presented in [24], where the sidelobe suppression approach
was improved by using a long time-domain cancellation signal spanning over a number of
consecutive OFDM symbols. This method results in an increase of BER due to increased
interference relative to the method presented in [23]. In [25], this method is improved by
introducing the constraint on the self-interference power level.
An interesting approach to the mitigation of OOB interference, called partial response
signaling (PRS) [26], makes the values on each subcarrier dependent on the subsequent
OFDM symbols. This can be done by independent lowpass filters on each input of the in-
verse fast Fourier transform (IFFT) block. Although relatively substantial OOB interference
suppression can be achieved even with very low order (2–3) filters, the reception of such a
signal requires either a slicer or a Viterbi detector when treating PRS filtering after being
influenced by the multipath propagation channel.
An observation that the OOB radiation is the result of the time domain non-continuity
between subsequent OFDM symbols was the basis for a spectrum shaping method presented
in [27]. This method is called N-continuous OFDM (NC). The continuity of 0th to Nth-order
derivatives at the ends of the OFDM symbols is achieved by adding low power, complex-
valued quantities to each active data subcarrier at the input of a IFFT block.

An entire class of methods that support the protection of PU signals from the effects of
OOB interference is based on the use of power allocation schemes that not only maximize the
throughput but also reduce the OOB interference power, e.g., refer to methods presented in
[2,28,29]. However, as these approaches might be seen as part of radio resources management
they will not be investigated here further.
Finally, the concept of modulated filterbanks (MFB) can be also successfully applied
to suppress the sidelobes of the OFDM transmission [30]. MFB can be used for sidelobe
suppression by applying them over the OFDM spectrum such that the series of bandpass
filters allows only the required spectrum to pass through it while rejecting the unwanted
OOB radiations in every subband.
9
3.2 Windowing
Windowing (W) is usually applied to the OFDM symbol time-domain samples with CP.
The use of windowing is shown in Figure 1. The time-domain OFDM symbol of duration
N + N
CP
samples, where N
CP
is the duration in samples of the CP, is extended cyclically
with β samples at the end of the considered symbol. This extension is referred to as the
cyclic suffix (CS). If we denote the time-domain signal for a single OFDM symbol as a vector
x = {x
−β−N
CP
, ,x
N−1+β
}, the OFDM-symbol time-domain samples are defined by vector
y = {y
k
}, which results from the multiplication of the vector x = {x

k
} by the window shape
w = {w
k
},namely:
y
k
= w
k
x
k
, (1)
where k = −β − N
CP
, ,N − 1+β. In [31], it has been shown that the largest sidelobe
suppression is achieved when the Hanning window is applied. In case of the Hanning window,
w = {w
k
} possesses the following form:
w
k
=
⎧
⎪
⎪
⎪
⎪
⎪
⎪
⎪

⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎩
0.5+0.5cos

π
k+N
CP
β

,k∈{−β − N
CP
, ,−1 − N
CP
}
1,k∈{−N
CP
, ,N − 1}
0.5+0.5cos

π
k−N

β

,k∈{N, ,N − 1+β}
. (2)
Referring to Figure 1, it is worth mentioning that to provide a relatively small throughput
decrease, consecutive symbols overlapping with each other by β samples can yield an effective
OFDM symbol duration of N + N
CP
+ β samples.
The primary advantages of this method is its low computational complexity, indepen-
dence of the modulated data, and its suitability for NC-OFDM. When employed by a CR
communication system attempting to access the available spectrum in a dynamically varying
radio environment, it is also important that the length and shape of the applied window can
be also altered dynamically. This method is the most suitable for minimizing the interfer-
ence in the PU transmission that is relatively distant in frequency from SU transmission
band [7,31]. The main drawback of this method is the decrease of throughput caused by the
addition of the CS.
10
3.3 Cancellation carriers
The CCs method [32] takes advantage of the spectrum shape of each subcarrier in order to
reduce the resulting OOB interference level. As each OFDM symbol possesses a limited time
duration, this can be interpreted as cutting out a part of an infinitely long OFDM symbol by a
rectangular window. In other words, the spectrum of each subcarrier is convolved with a sinc
function, thus widening the spectral overlapping regions with the other subcarrier spectra.
Although this is generally the primary reason for the existence of high OOB interference, this
phenomenon can be manipulated in a positive fashion using the CCs method, where a subset
of active subcarriers are selected for the sole purpose of cancelling the OOB interference of
the adjacent subcarriers. As the subcarriers closest to the spectrum edge have the strongest
influence on the OOB radiation, they are usually chosen to carry the cancelling signal, and
they do not support any data transmissions themselves. The sidelobes of these subcarriers

are intended to negatively combine with the sum of the sidelobes resulting from the active
data-bearing subcarriers, thus potentially reducing the overall OOB interference levels as
shown in Figure 2.
The values of the cancellation subcarriers have to be calculated for each OFDM symbol
separately since the independent modulated data symbols cause different OOB interference
levels. Thus, several frequency-sampling points are defined in order to determine the values of
the OOB signal spectrum at the corresponding frequencies. These frequency-sampling points
describe the optimization region in which the estimates of the spectrum values resulting from
the spectral superposition of the data subcarriers (DCs) and the CCs have to be calculated.
The optimization problem to be solved for each OFDM symbol can be defined as follows:
min
s
c



P
(δ×γ)
CC
s
c
+ P
(δ×α)
DC
s
d



2

, (3)
where P
(δ×γ)
CC
is the matrix of dimensions (δ × γ) transforming the vector of the CCs values
s
c
of length γ to the spectrum estimates. For the DCs, the matrix P
(δ×α)
DC
of dimensions
(δ × α) and vector s
d
of length α perform the same role. However, as the authors of this
method have found, such an optimization approach may result in a higher power level for
the CCs relative to the DCs. Consequently, the additional constraint has been introduced
to limit this effect:
s
c

2
≤ Π
CC
, (4)
11
where Π
CC
is the maximum allowable power for CCs. Although the solution of (3) is widely
known, and has been presented in [33, 34], the constraint (4) increases the computational
complexity of the optimization problem significantly, requiring us to solve the Lagrange

inequality for each OFDM symbol, which might become infeasible for wideband transmissions
possessing a large number of subcarriers.
Another drawback of the CC method, apart from the computational complexity, is the
link-performance deterioration, i.e., an increase of the BER. This is due to the fact that
an OFDM system usually operates under the total power constraint. If part of an OFDM
symbol energy is sacrificed to the CCs, the remaining energy that can be used for data
transmission is reduced, and this naturally results in an SNR loss and corresponding BER
degradation.
Nevertheless, the CC algorithm has been extensively investigated, and a number of mod-
ifications and combinations of the CC algorithm with other methods has been presented in
the literature, e.g., refer to proposed approaches in [35–37]. For example, active interfer-
ence cancellation (AIC) [33] is a method similar to CCs solution, where in addition to the
OFDM edge-subcarriers several other subcarriers inside the PU transmission band are also
used to minimize the OOB radiation. However, as shown in the aforementioned paper, the
AIC subcarriers inside the PU transmission band possess a negligible influence on the OOB
interference. Moreover, they can significantly increase the computational complexity of the
resulting implementation. The CCs method is very flexible in terms of defining the number
of cancellation subcarriers and their power levels. Moreover, some of its shortcomings can
be efficiently equalized if the W method with a parameter-defined window duration is also
applied.
In Table 1, the main properties of the OOB power reduction methods discussed in this
section are summarized. From this summary, we can conclude that the methods best-suited
for the application in CR and the DSA networks are the CC and W methods. Moreover,
the combination of these two methods have the potential for some promising performance
improvements in terms of flexibility. In the next section, we discuss the combination of
these methods in detail, and propose new algorithm that allows for reduction of its com-
putational complexity, reduction of the power assigned to the CCs (considered wasted for
12
the transmission of symbols bearing no information), improvement of BER and thus, for the
implementation of this algorithm in practical systems.

4 Advances of the state-of-the-art in the OOB power reduction:
promising combination of windowing and CCs technique
4.1 Reduced-complexity reduced-power combined CCs and windowing
The combination of CCs with windowing seems to be a promising spectrum shaping mech-
anism. While windowing method provides better OOB interference mitigation for spectrum
components more distant from occupied OFDM band on the frequency axis, the CCs method
has the same behavior for components closer to the OFDM nominal band as shown in [34].
Thus, the combination of both methods, which was also presented in [34], provides additional
degrees of freedom as the number of CCs and window shapes can be altered to fulfill the
transmission requirements. In this section, we present several additional enhancements to
this combined approach, thus yielding a reduction in the computational complexity, reduc-
tion of the energy-loss (energy inefficiency) due to the use of the CCs, and an improvement
of the BER performance.
The system that we consider in this research consists of a conventional OFDM modulator,
where the CCs unit, which performs the CCs algorithm, is employed prior to the N-size
IFFT block, and windowing is applied to the time domain signal after extending it with the
CP. The resulting OFDM-modulated signal after the OOB interference reduction process is
then fed to the digital-to-analog converter and the IF/RF (Intermediate Frequency/Radio
Frequency) front-end.
The CCs optimization formula must be designed for the time domain windowed signal y.
Suppose we denote the input of the IFFT as the vector s = {s
−N/2
, ,s
N/2−1
}, which con-
tains zeros except for the elements indexed as c = {c
1
, ,c
γ
} and d = {d

1
, ,d
α
},where
the CCs symbol values and the data symbols are inserted, respectively. The optimization of
cancellation symbols is based on the estimation of the spectrum values resulting from the
superposition of the spectra of each CC and DC. The time-domain OFDM symbol vector y
13
elements can be mathematically expressed as:
y
k
= w
k
N/2−1

n=−N/2
s
n
exp

j2π
nk
N

, (5)
where this OFDM-symbol spectrum estimate at frequency bin l can be derived by preforming
the M -times upsampled FFT for this point b
l
using the following approach:
b

l
=
N+β−1

k=−N
CP
−β
w
k
N/2−1

n=−N/2
s
n
exp

j2π
nk
N

exp

−j2π
lk
NM

=
N/2−1

n=−N/2

s
n
N+β−1

k=−N
CP
−β
w
k
exp

j2π
k
N

n −
l
M

=
N/2−1

n=−N/2
s
n
p
n,l
.
(6)
For a set of frequency-sampling points l = {l

1
, ,l
δ
} defined in the optimization region,
and for n ∈ c, the coefficients p
n,l
are the elements of the matrix P
(δ×γ)
CC
,andcanbepre-
calculated. Similarly, for the data carriers, when n ∈ d, p
n,l
defines the matrix P
(δ×α)
DC
,and
can be calculated off-line.
The commonly used optimization problem definition can be expressed using the Equation
(3). Recall that the aim of this research is to minimize the OOB interference level, which
implies solving the following optimization framework:
min
s
c



P
(δ×γ)
CC
s

c
+ P
(δ×α)
DC
s
d



2
, (7)
where s
d
and s
c
contain complex values modulating data carriers and CCs respectively, and
form the subvectors of vector s created by the d and c indexed cells, respectively. The
solution of this problem yields the values of CCs s
c
,namely:
s
c
= −P
(δ×γ)
CC
+
P
(δ×α)
DC
s

d
, (8)
where []
+
denotes the pseudoinverse. Although such a solution is relatively fast with respect
to computational complexity, since the multiplication of vector s
d
by a precalculated matrix
is performed for each OFDM symbol, it suffers from several issues.
14
First, as shown in [32], several parts of the OFDM symbol power will need to be allocated
to the CCs. Thus, in practical systems with an appropriately chosen power constraint, the
SNR for the data carriers is reduced by the estimated value:
ξ =10log
10

s
c

2
+ s
d

2
s
d

2

. (9)

The reference system in this case is the one that employs the nulled guard subcarriers on the
subcarriers used by the CCs method in the proposed system. Another significant drawback
is a substantial increase of the PAPR that is caused by the high power values transmitted
on the CCs correlated with the DCs. Apart from the PAPR value, usually the probability of
peaks occurrence is also taken into account since it is conceivable that the time-domain peaks
possessing moderate instantaneous power can cause nonlinear distortions and performance
deterioration that can prove to be much worse than the high power (strong) peaks occurring
relatively infrequently. On the basis of this observation, the PAPR is measured with a
certain probability p
PAPR
. We will determine this metric later in this section when providing
simulation results for a probability of p
PAPR
=10
−3
.
Finally, an important phenomenon when applying the CC method for OOB interference
reduction is the occurrence of frequency-domain power peaks for frequencies assigned to the
CCs. This is due to the fact that the CCs have to compensate for a number of DC sidelobes.
A large power increase at the edges of the NC-OFDM frequency spectrum (where CCs are
located) may be unacceptable according to the existing regulations that impose constraints
on the transmission spectral masks. In order to provide some metric reflecting this problem,
let us define the spectrum overshooting ratio (SOR) for a given probability p
SOR
of exceeding
level  of the spectrum mask by the CCs power:
SOR = 10 log
10

arg


[Pr(S(f
CC
) >)=p
SOR
]
1
B
SU−CC

B
SU−CC
S(f ) df

, (10)
where S(f) is the power spectral density (PSD) function of the considered NC-OFDM
secondary-user signal, B
SU−CC
is the bandwidth of the considered NC-OFDM secondary-
user transmission used by the data subcarriers (excluding cancellation subcarriers frequency
bands), and f
CC
is any one of the frequencies belonging to CCs bands. This definition for the
SOR can be interpreted as the logarithm of the PSD peaks of CCs with respect to the mean
15
power level in data carriers band. The occurrence of these peaks is measured with probabil-
ity p
SOR
. Note that in simulation results presented in the next subsection, p
SOR

=10
−1
will
be considered. This probabilistic approach is required to take a varying characteristic of the
PSD estimate into account.
To overcome the aforementioned problem with respect to an unacceptable power increase,
we propose to supplement the optimization problem described by (7) with an additional,
indirect constraint whose aim is to minimize the CCs power. The optimization problem is
now defined as follows:
min
s
c




P
(δ×γ)
CC
s
c
+ P
(δ×α)
DC
s
d



2

+ μ s
c

2

, (11)
where μ factor is used to balance between the CCs power and resulting OOB power reduction.
The solution of this problem can be derived by merging both conditions and related matrix
operations, and results in the following vector of CCs, namely:
s
c
= −
⎛
⎜
⎜
⎝
P
(δ×γ)
CC
√
μI
(γ×γ)
⎞
⎟
⎟
⎠
+
⎛
⎜
⎜

⎝
P
(δ×α)
DC
0
⎞
⎟
⎟
⎠
s
d
= Ws
d
, (12)
where I
(γ×γ)
is a γ-size identity matrix, and W results from multiplication of the first two
matrices in the above equation. Such an optimization has similar computational complexity
to the optimization problem of (7), as only once for a given spectrum mask, and after the
number of DCs and CCs are determined, the optimization (calculation of matrix W)is
implemented. Then, for each OFDM symbol, matrix-by-vector multiplication is carried out
with pre-calculated matrix W elements. The performance and influence on various system
parameters will be evaluated in the next section.
The optimization procedure described above significantly reduces the SNR loss typically
found for a CCs method. This is obtained as a result of imposing a constraint on the
value of the SOR, which consequently reduces the power assigned to CCs and increases
power reserved for the DCs. Nevertheless, the reduced power available for data subcarriers
still cause some deterioration of the reception quality. Therefore, we propose the following
reception technique that makes use of the CCs inherent redundancy.
As the CCs are correlated with data symbols, these additional subcarriers can be used

in the signal reception that might not only regain the power devoted to these subcarriers
16
in the first place, but also make use of the frequency diversity for achieving a higher degree
of robustness with respect to the frequency-selective fading. Let us consider Equation (12)
as a process of generating redundancy symbols s
c
transmitted in parallel to data symbols
s
d
. This operation is conducted on the complex symbols, thus allowing us to employ the
theory of complex-field block codes [38] for this problem formulation. To do so, let us
rewrite Equation (12) in order to determine the systematic code generation matrix G of size
(γ + α × α), namely:
G =
⎛
⎜
⎜
⎝
I
α×α
W
⎞
⎟
⎟
⎠
. (13)
By changing the row order of presented matrix, the order of data and cancellation symbols
can be kept, but for simplicity we will skip this operation. A simple reception mechanism
designed for such codes is based on the zero-forcing criterion, for which the reception matrix
is defined as:

R =(HG)
+
, (14)
where H is (γ+α×γ+α) diagonal matrix with channel coefficients for each of used subcarriers
on its diagonal. This matrix should be used in the receiver after the FFT processing instead
of an equalizer used in standard reception chain. The estimate of the data symbols

s
d
is
achieved by the following operation:

s
d
= R˜s
d+c
, (15)
where ˜s
d+c
is a received vertical vector at the output of FFT block containing distorted
and noisy values of data and cancellation subcarriers. Although the calculation of matrix
R can be quite complex, it needs to be performed only once for each channel instance and
subcarrier pattern. Moreover, with a systematic code implementation, this method may be
treated as optional, reserved only for high performance, high quality reception.
Finally, let us derive a metric that indicates the potential throughput loss caused by
introduction of CCs, windowing or the combination of CCs and W. This throughput loss
can be assessed in comparison to a system not employing any OOB interference reduction
method, in which all subcarriers are occupied by the DCs. Note that the actual system
throughput depends not only on the number of data subcarriers but also on the power
17

assigned to these subcarriers and the channel characteristic observed. Therefore, this metric
indicates only potential throughput loss that results from the information signal bandwidth
reduction due to introduction of the CCs and window duration extension, thus assuming the
same transmit power and channel quality at each subcarrier. It is described by the following
expression:
R
loss
=

1 −
1 −
γ
γ+α
1+
β
N+N
CP

· 100%. (16)
Note that the reference system for this definition, i.e., all subcarriers employed for data trans-
mission, is prohibited from operating in the considered scenario, where the PU transmission
protection is required and the SU sidelobes have to be reduced.
4.1.1 Simulation results
Below, we present the Monte Carlo simulation results using MATLAB and showing that
our introduced modifications of the combined CC and W method improves the overall
performance of the NC-OFDM system in several ways. In our experiments, we assumed
N = 256 subcarriers, where the subcarriers possessing the indices d = {−100, ,−62}∪
{−41, ,−11}∪{10, ,40}∪{61, ,101} are occupied by the QPSK data symbols, and
there are three CCs placed on each side of data carriers blocks, i.e. c = {−103, −102, −101}∪
{−10, −9, −8}∪{7, 8, 9}∪{41, 42, 43}∪{58, 59, 60}∪{102, 103, 104}. The subcarriers pattern

of four data subcarrier blocks is separated with narrowband PUs, e.g., program making and
special events (PMSE) devices such as professional wireless microphones with bandwidth of
200 kHz. Note that an explanation of the wideband and narrowband PU signals and sce-
narios under consideration with respect to the coexistence of the PU and SU transmissions
are given in the next section, with the real-world experimental results. The duration of the
CP equals N
CP
= 16 samples, but the β = 16 samples of the Hanning window extension
(equal to CS) are also used on each side of an OFDM symbol. The number of CCs and
shaping window duration was chosen in such a way that the mean OOB interference power
level is achieved at least 40 dB below the mean in-band power level for reasonable value
of μ, i.e., μ =0.01. This OOB power attenuation is sufficient in order to respect several
regulatory spectrum masks, e.g., IEEE802.11g [39] or LTE user-equipment [40] Spectrum
18
Emission Mask (SEM).
First, in Figure 3, we show the results of the OOB power reduction obtained for the
following three methods under consideration: CC method, windowing, and combined CC
and W scheme. The comparison has been performed for the schemes that present the same
potential throughput loss metric, which for our evaluation system equals R
loss
=19.2%.
Such a potential throughput loss is obtained either from the CC method with γ
e
= 4 CCs
per edge of the DCs band, from the W method with Hanning window extension of β =65
samples, or from the combined CCs and W method with γ
e
=3andβ = 16, i.e. the scenario
described above. The PSDs were obtained for the signal before HPA using Welch’s method
after transmitting 10,000 random OFDM symbols. The spectrum was estimated in 4N

frequency sampling points using 3N-length Hanning windows. Note, that the windowing
method achieves a high OOB power attenuation, but it requires several frequency guard
bands for the OOB attenuation slope. Thus, it is potentially unsuitable for protecting
narrowband PU signals from unintentional secondary OB interference. Conversely, the CC
method alone results in a relatively steep OOB power reduction, but the resulting OOB
attenuation is not very high. The combination of both methods provides decent performance
in terms of high and steep OOB attenuation, thus confirming that such a combination of
these methods possesses the potential for protecting both wideband and narrowband PU
signals employing strict requirements with respect to the signal-to-interference-power ratio.
According to our other experiments for QAM/PSK schemes and to their results not presented
here, the normalized PSD plots are very similar.
In Figure 4, several of the system performance metrics described above, such as the
SOR for p
SOR
=10
−1
, PAPR increase for p
PAPR
=10
−3
, SNR loss for BER= 10
−4
,and
OOB power attenuation, are presented in relation to the optimization constraint parameter
μ ∈10
−6
, 10
0
. Thus, the optimization procedure is considered in the range of μ, defin-
ing scenarios from a weak constraint on the CCs power, close to no power-constraint, to a

constraint on the strictly limited CCs power. For these performance metrics under consider-
ation, measurements have been obtained after the transmission of 2 × 10
5
OFDM symbols.
The SNR loss has been calculated at the receiver, for an example 4-paths Rayleigh-fading
channel defined in Case 3 test scenario for UMTS user equipment [41]. Averaging of the
19
results has been done using 10,000 channel realizations.
It can be observed in Figure 4 that the OOB power attenuation decreases slowly with an
increase of μ for small values of μ.Thus,whenμ is low, there is no use in spending additional
power on CCs since the spurious OOB emissions remain the same. On the other hand, low-
power CCs (for high μ values) do not provide improvement in OOB power over results
obtained for windowing method without the application of CCs. However, the other metrics
improve when μ increases. For example, the fluctuation of SOR ranges from 13.7 to −3.9dB.
It is worth mentioning that the rest of the system performance metrics are calculated with
respect to the reference system, which does not use windowing and CCs for OOB power
reduction. Instead, the CCs are replaced with zeros. The significant improvement is observed
in PAPR-increase value that approaches zero, when μ becomes high. Both new optimization
goal defined by (11), and proposed reception algorithm have influence on the values of an
SNR loss with standard detection and with our proposed detection making use of the CCs
redundancy. The stronger the limit is on the CCs power (the higher μ)theDCspoweris
not wasted as much on the CCs. Thus, the SNR loss changes from 4.8 dB for μ =10
−6
to
nearly 0 dB for μ =10
0
.
The results after employing our proposed detection method show that not only do the
DC power levels reassigned to the CCs was recovered, but also an additional improvement
was achieved thanks in part to the frequency diversity introduced by CCs treated as parity

symbols of the block code. We observe that the coding gain for BER = 10
−4
(with respect
to system without CCs) varies from 6.42 dB for μ =4× 10
−6
to 0.7 dB for μ =1. Forvery
low values of μ (μ<4 × 10
−6
), the SNR loss caused by the introduction of CCs becomes
higher than can be compensated for even by using high power CCs, which yields a coding
gain decrease. The results presented in Figure 4 show that our reception algorithm making
use of the CCs redundancy yields decent performance even in the assumed case of large
fragmentation of available (not occupied by the PUs) frequency bands.
20
5 Real-world experimental results
5.1 Implementation setup
One application for the deployment of CR systems and DSA networks is the opportunis-
tic spectral usage of unoccupied portions of the TV frequency bands by future mobile ra-
dio systems such as long term evolution (LTE) mobile radio communication [42]. A tele-
vision whitespace (TVWS) is a region of wireless frequencies where several digital video
broadcasting-terrestrial (DVB-T) channels are not used by a licensed transmission, and
therefore can be temporarily borrowed by TVWS devices capable of operating in these
bands as long as they respect the limits concerning the maximum allowable transmit power
in this area, as well as the level of their OOB interference power.
Moreover, it is envisioned that wireless microphones will be operating in these TVWS
regions and associated frequency bands, as well as other wireless devices used for PMSE [43].
Although several spectrum regulators anticipate reserving one TV channel for the exclusive
access by PMSE equipment (e.g., U.K. Ofcom reserves channel 38), it is anticipated to be not
sufficient for large events that commonly use over 100 wireless microphones. Hence, PMSE
devices may be using other channels as well. In order to address this application scenario,

we consider the PU signals in this scenario to consist of a DVB-T transmission using an
8 MHz channel and a PMSE transmission possessing a 200 kHz bandwith. Moreover, the
LTE transmissions are considered to be SU signals in these frequency bands. In particular,
our tested system implements the LTE-like transmission with N = 512 subcarriers, with the
possibility of turning some of the subcarriers off, as well as using some of them as the CCs
for OOB interference reduction. The subcarrier spacing is 15 kHz, and the useful spanned
band is 7.68 MHz. The CP duration in samples N
CP
= N/16 has been used, and the binary
phase shift keying (BPSK) signalling has been applied at each subcarrier.
Note that for our SU system the PMSE band spans over 14 subcarriers, and therefore such
a PMSE transmission is considered as narrowband PU signal. If we consider channelization
of the available subcarriers in blocks of 16 subcarriers, one block of subcarriers has to be
deactivated in order to protect such a narrowband PU signal when detected. Our second
type of PU signal, the DVB-T system uses at minimum 8 MHz channel, and thus more than
the assumed SU bandwidth of 7.68 MHz. Therefore, it is considered to be a wideband PU
21
signal, and if such a PU signal is detected, its channel must remain unoccupied by the SU
system.
Employing the assumption that the LTE system is considered to be an SU signal, we
investigate the OOB interference suppression taken from this system SEM. In particular,
our goal is to achieve a 59 dB OOB interference power attenuation below the PSD level of
data carriers for the first use-case of the high-power transmitter and call it use-case 1.Note,
that the minimum required suppression defined in the LTE Base Station (BS) SEM employs
this value, i.e., 59 dB. In our second use-case, called use-case 2, 26 dB will be the required
OOB power attenuation, which is the typical value for the LTE user equipment transmitter
that has to be obeyed in adjacent channels. Note that in order to protect various types
of PU signals present in the spectrum, their required signal-to-interference ratio must be
considered together with the signal attenuation between the SU transmitter and the PU
receiver. Moreover, the PU receive filters parameters and their sensitivity have to be taken

into account.
It is envisioned that the TVWS geolocation databases will provide the information on the
maximum in-band and OOB power allowable at the specific location for the specific devices
and services. Here, we assume that the PU signals and SU signals are located at a distance
that allows the use of the standard SEMs for the reduction of the OOB interference. Never-
theless, our proposed shaping mechanism is designed so that it can fit flexibly to any existing
SEM requirement and the OOB power suppression requirements can be changed dynami-
cally when a new PU is detected in the adjacent channel or in the middle of transmission
band of the SU.
In order to evaluate the flexibility of our spectrum shaping algorithms, we consider the
following four scenarios of the PU and SU coexistence, namely:
• Scenario 1 : The SU system occupies continuous bandwidth, with only DC carrier
turned off. The DVB-T systems or densely located PMSE devices (the PUs) are
detected to operate on both sides of the SU’s band, which uses subcarriers of indices:
{−100, ,−1}∪{1, ,50}. The OOB power reduction mechanisms have to be used
on both sides of the SU band.
• Scenario 2 : Outer wide-band PUs (DVB-T) are detected and one narrow-band PU
22
(PMSE device) in the middle of the SU transmission band (16 subcarriers turned off).
The indices of the SU’s used subcarriers are: {−100, ,−8}∪{9, ,50}.
• Scenario 3 : Outer wide-band PUs (DVB-T) are detected and two narrow-band PU
using and non-contiguous bands (PMSE devices) inside the SU’s band. The indices of
the SU’s used subcarriers are: {−100, ,−8}∪{9, ,50}∪{67, ,100}.
In each scenario, the same number of CCs are committed to each edge of the data
subcarriers blocks, e.g., two CCs are used at each edge of the data block in scenario 2, thus the
indices of the CCs are: {−100, −99, −9, −8, 9, 10, 49, 50}. For the purpose of the spectrum
windowing, the Hanning window has been chosen due to its relatively high OOB interference
attenuation. The parameters of our experimental OFDM-based secondary wireless access
system implemented for this project are given in Table 2. The potential rate-loss values due
to the usage of the CCs (defined by (16)) are also presented.

5.2 Experiment outcomes
The spectrally agile OFDM experimental testbed developed at Poznan University of Tech-
nology utilizes the IRIS SDR platform [44]. IRIS was developed at Trinity College Dublin,
and is a GPP-based rapid prototyping and deployment system. The building blocks of the
radio components in a transceiver chain are written in C++. Extensible Markup Language
(XML) is used to specify the signal chain construction and characteristics. The usability of
this platform for demonstration of the OFDM signal spectrum shaping based on filtering has
been described in [45]. Using this testbed, the IRIS SDR platform has been used in conjunc-
tion with the RF hardware front-end USRP N210 and its daughterboard XCVR2450. The
transmitted signal spectrum at the output of the USRP front-end has been measured using
a Rhode & Schwarz spectrum analyzer. Additionally, the transmit signal PSD at the output
of the IRIS SW platform (at the input of the USRP) has been analyzed in MATLAB. The
implementation setup of this testbed is shown in Figure 5.
In order to allow for the online reconfiguration, computationally efficient fast algorithm
for optimization described in Section 4 has been applied, whose solution is given by formula
(12). The most computationally complex operation, the matrix pseudoinverse, has been per-
23
formed using CLAPACK [46] library. Other operations, i.e., matrix-matrix or matrix-vector
multiplication, have been performed using self-built functions. However, their performance
can be improved using low level specialized libraries such as BLAS. Our SU transmitter
has been constructed to be fully reconfigurable, i.e., the indices of used data carriers, the
numbers of CCs at each subcarriers block edge γ
e
, or the window duration can be changed
through the XML file.
The USRP interpolates the transmit signal in the field programmable gate array (FPGA)
unit, but the interpolation filters are not very flat in their passband, especially when higher
sampling rates are used, and therefore, some stages of the interpolation filters have to be
turned off. To present reliable results, this threat had to be avoided, and thus the sampling
rate was limited to 5 MSps. This changes the subcarriers spacing to about 9.8 kHz while

other parameters of OFDM modulation, e.g., CP length or number of subcarriers, are kept
the same. Moreover, the PU bandwidth was also proportionally downscaled. Thus, the
subcarriers block that needs to be turned off to protect the PMSE device (our narrow-band
PU) still consisted of 16 subcarriers. The parameters of our CR transmitter implemented on
the IRIS platform and of the Rhode & Schwarz spectrum analyzer used in the experiments
are shown in Table 3.
In Figures 6, 7, 8, 9, 10 and 11, we present the results of our OOB power reduction scheme
based on the modified CCs method combined with Windowing and applying new algorithms
combating the spectrum overshooting problem and reducing computational complexity. Note
that all these modifications and improvements necessary for practical implementation on a
CR platform have been described in Section 4. Figures 6, 7, 8, 9, 10 and 11 show the
PSDs of the OFDM (or NC-OFDM) SU transmissions under evaluation in our CR system
with the wideband (DVB-T) and narrowband (PMSE) PU signals being protected from
the interference of the high-power (BS) and low-power (user-equipment) SU transmitters.
These figures show the results obtained from the scenarios under consideration (assuming the
existence of PU signals) and use-cases (SEMs assumed), described in the previous subsection.
The red curves present the PSD of the NC-OFDM signals with deactivated subcarriers, i.e.,
without any spectrum shaping method. In each plot, one can see that the sidelobe levels of
the SU transmit signal without OOB power reduction possess relatively high power levels,
24